scholarly journals A Review on Big Data Analytics Via Social Media

2019 ◽  
pp. 299-302
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Social Media Analytics ◽  
Data Mapping ◽  
Social Data ◽  
Advantages And Disadvantages ◽  
Business Change ◽  
Social Media Platforms ◽  
The Right

Analytics is very important in all fields in order to make decisions over certain facts. Social media analytics is the process of collecting information from various social media platforms, websites and blogs. These analytics is done to make effective business conclusions. The usage of social media has become the latest trend in today’s world. Social data analytics is not about just collecting likes and comments shared by individuals but it has become the platform for many trademarks to bring out promotion. Applications such as marketing, elections widely used social data to make predictive decisions. Some of the approaches followed are forming hypothesis, getting deep into the data, mapping events etc. These analytics can also be done in applications such as business, Change in amendments, Education, Demonetization etc. The challenges faced are metrics formed by social media should reach the right people, unstructured data being difficult to priestship paper discusses about the model, theme, performance evaluation, advantages and disadvantages under literature survey.

scholarly journals Understanding an Urban Park through Big Data

2019 ◽  
Vol 16 (20) ◽  
pp. 3816 ◽  
Author(s):  
Jisoo Sim ◽  
Patrick Miller
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Planning Process ◽  
Big Data Analytics ◽  
Urban Park ◽  
Social Media Analytics ◽  
Forest Park ◽  
Planning And Design ◽  
Data Source

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

scholarly journals Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kiran Chaudhary ◽  
Mansaf Alam ◽  
Mabrook S. Al-Rakhami ◽  
Abdu Gumaei
Keyword(s):  
Social Media ◽  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Process Data ◽  
Social Media Platform ◽  
The Social ◽  
Social Media Platforms ◽  
Media Platform

AbstractSocial media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.

scholarly journals In the Interest(s) of Many: Governing Data in Crises

2020 ◽  
Vol 8 (4) ◽  
pp. 421-431
Author(s):  
Nathan Clark ◽  
Kristoffer Albris
Keyword(s):  
Data Analytics ◽  
Big Data Analytics ◽  
Sensitive Data ◽  
Global Policy ◽  
The Right To Privacy ◽  
Social Media Platforms ◽  
Key Issues ◽  
The Right ◽  
Data Subject ◽  
Realist Perspective

The use of digital technologies, social media platforms, and (big) data analytics is reshaping crisis management in the 21st century. In turn, the sharing, collecting, and monitoring of personal and potentially sensitive data during crises has become a central matter of interest and concern which governments, emergency management and humanitarian professionals, and researchers are increasingly addressing. This article asks if these rapidly advancing challenges can be governed in the same ways that data is governed in periods of normalcy. By applying a political realist perspective, we argue that governing data in crises is challenged by state interests and by the complexity of other actors with interests of their own. The article focuses on three key issues: 1) vital interests of the data subject vis-à-vis the right to privacy; 2) the possibilities and limits of an international or global policy on data protection vis-à-vis the interests of states; and 3) the complexity of actors involved in the protection of data. In doing so, we highlight a number of recent cases in which the problems of governing data in crises have become visible.

What Determines User-Generated Content Creation of College Football? A Big-Data Analysis of Structural Influences

2018 ◽  
Vol 11 (2) ◽  
pp. 219-240 ◽  
Author(s):  
Grace Yan ◽  
Dustin Steller ◽  
Nicholas M. Watanabe ◽  
Nels Popp
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Structuration Theory ◽  
College Football ◽  
User Generated Content ◽  
Hierarchical Regression ◽  
Content Creation ◽  
Social Media Platforms

The question of how and why users engage in sport digital communication endures. In this study, structuration theory is employed to examine how social-media users exercise preferences in the creation of content as they respond to a variety of macrolevel factors pertaining to college football—the type of game, team strength, conference membership, market characteristics, etc. Through hierarchical regression analysis, the results indicate that the presence and timing of college football games, as well as team strength and game outcome, are significant determinants for the patterns of online content generation. As such, the study advances the theoretical, methodological, and managerial inquiry of user-generated content on sport social-media platforms through a Big Data analytics approach.

Big Data Analytics in Social Media

2019 ◽  
pp. 107-124
Author(s):  
Janani Balakumar ◽  
Vijayarani Mohan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Information Control ◽  
Social Media Platforms ◽  
Current Scenario ◽  
Processing Techniques

The rapid development of online social media is the method of collaboratively produced content material presents new possibilities and challenges to both producers and patrons of knowledge. The term big data refers to large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques. In the current scenario, social media has gained amazing attention within the last decade. Accessing social media platforms and websites such as Facebook, Twitter, YouTube, LinkedIn, Instagram, and Google+, web technologies have become more responsible. People are becoming more fascinated about and relying on social media platform for records, news, and opinion of other customers on diverse topics. Hence, these situations produce a large volume of data. The main objective of this chapter is to provide knowledge about big data analytics in social media. A brief overview of big data and social media are discussed. Research challenges in social media are also discussed.

scholarly journals A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

2021 ◽  
Vol 16 (4) ◽  
pp. 82
Author(s):  
Muhamad Hariz Muhamad Adnan ◽  
Shamsul Arrieya Ariffin ◽  
Hafizul Fahri Hanafi ◽  
Mohd Shahid Husain ◽  
Ismail Yusuf Panessai
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Role Model ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Vocational Education And Training ◽  
Social Media Analytics ◽  
Prospective Students

Recently, the promotion of Science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective students’ interest in STEM and also Technical and Vocational Education and Training (TVET) subjects. However, very little research has been done for the higher education institutions in Malaysia in leveraging social media and social media analytics effectively to increase the students’ interests and awareness of STEM and TVET disciplines. Therefore, this paper aims to propose a framework to increase prospective students’ interest in STEM and TVET using social media and big data analytics. The objectives of this study are to explore various social media applications in education and study these applications towards increasing students’ interests and propose a suitable framework for Malaysian higher education institutions. The framework is proposed by following the theory synthesis methodology. Four main components of the framework have been proposed, namely social media, role model or mentoring, massive open online courses and big data analytics. Each component is significant and requires a considerable amount of time to develop. The suggested framework is anticipated to benefit higher education institutions with a significant gain of the number of students, revenues and positive reputations.   Keywords: Social media, Social media analytics, STEM, E-learning, Education  

Chinese Social Media Platforms and Microblogging in the Context of Public Diplomacy

Communicology ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 167-179
Author(s):  
E.S. Nadezhkina
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Public Diplomacy ◽  
User Generated Content ◽  
The Internet ◽  
Interactive Communication ◽  
Network Interaction ◽  
Social Media Platforms ◽  
The Right ◽  
Chinese Social Media

The term “digital public diplomacy” that appeared in the 21st century owes much to the emergence and development of the concept of Web 2.0 (interactive communication on the Internet). The principle of network interaction, in which the system becomes better with an increase in the number of users and the creation of user-generated content, made it possible to create social media platforms where news and entertainment content is created and moderated by the user. Such platforms have become an expression of the opinions of various groups of people in many countries of the world, including China. The Chinese segment of the Internet is “closed”, and many popular Western services are blocked in it. Studying the structure of Chinese social media platforms and microblogging, as well as analyzing targeted content is necessary to understand China’s public opinion, choose the right message channels and receive feedback for promoting the country’s public diplomacy. This paper reveals the main Chinese social media platforms and microblogging and provides the assessment of their popularity, as well as possibility of analyzing China’s public opinion based on “listening” to social media platforms and microblogging.

scholarly journals EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

2021 ◽  
pp. 074391562199967
Author(s):  
Raffaello Rossi ◽  
Agnes Nairn ◽  
Josh Smith ◽  
Christopher Inskip
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Volume Content ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Regulatory Compliance ◽  
The Internet ◽  
Policy Recommendations ◽  
Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

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